Top Machine Learning Engineer Jobs in Boston, MA

Lead applied AI model post-training, evaluation, and production ML system development. Adapt models to customer-specific scientific workflows using SFT and reinforcement learning, build evaluation and monitoring systems, debug complex model behavior, collaborate with research and software teams, and mentor engineers. The role requires deep expertise in Python, PyTorch, LLMs, multimodal or agentic systems, and shipping reliable machine learning capabilities.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Develops and leads production machine learning and image processing solutions for neurovascular OCT and angiographic medical imaging. Responsibilities span algorithm research, prototyping, software architecture, implementation, testing, validation, deployment, and maintenance within regulated medical device systems. The role provides technical mentorship, contributes to ML frameworks and infrastructure, collaborates cross-functionally, performs code reviews, and supports quality, documentation, and compliance initiatives.

Lead the design, optimization, and production deployment of high-performance LLM inference systems using vLLM, CUDA, Triton, C++, and Python. Improve GPU utilization, latency, throughput, scalability, and cost through kernel optimization, quantization, batching, memory management, distributed inference, and profiling. Contribute to upstream open-source projects, establish engineering standards, review performance-critical code, and guide research-driven serving improvements.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Builds and operates production AI, machine learning, generative AI, and agentic systems. Responsibilities include designing ML architectures, developing models, implementing MLOps and LLMOps pipelines, monitoring production performance, establishing AI governance, and partnering with data engineering teams. The role sets technical standards, mentors engineers, drives scalable Databricks and Azure solutions, and influences architecture across multiple project and product teams.

Productionize machine learning systems for large-scale robotic fleets, including deployment infrastructure, predictive robot health monitoring, data pipelines, model validation, telemetry analysis, and fleet-wide rollouts. Collaborate with researchers and cross-functional engineering teams to deploy computer vision and ML models using cloud, edge, and GPU-accelerated platforms. Build simulation environments, dashboards, observability solutions, and scalable CI/CD processes while contributing hands-on Python development.

Lead the architecture, deployment, and optimization of scalable machine learning serving systems for real-time and batch workloads. Build CI/CD pipelines, monitoring, model versioning, and reliable MLOps infrastructure. Manage AWS cloud infrastructure, collaborate with data scientists and engineering teams, and support production systems through on-call rotation. The role also involves improving observability, reproducibility, security, and deployment reliability, with opportunities to deploy LLMs and distributed ML systems.

Lead the architecture and development of production AI/ML systems, including LLM applications, RAG, tool use, multi-agent workflows, and real-time inference. Partner with product, design, data, and leadership to ship scalable capabilities. Implement AI security, safety, privacy, reliability, observability, and human-in-the-loop controls. Mentor engineers and help shape technical practices in a fast-moving fintech startup.

Build and deploy NLP and LLM-based machine learning models for an AI-powered risk intelligence platform. Responsibilities include text classification, entity extraction, clustering, topic detection, RAG, agentic workflows, semantic search, model evaluation, production monitoring, retraining, and scalable ML service deployment. Collaborate remotely with data engineers, backend engineers, and product stakeholders on high-volume, multilingual text data.

Build software connecting Fabri’s digital manufacturing process, including manufacturing data collection, process analytics, workflow automation, machine integration, backend services, and internal tools. Develop and deploy machine-learning models for outcome prediction, anomaly detection, inspection, and process monitoring. Analyze process data and geometry, improve manufacturing quality and throughput, and collaborate directly with operators, engineers, and manufacturing teams in an on-site factory environment.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Leads the evaluation, design, development, and deployment of production AI/ML applications in AWS environments. Builds and optimizes machine learning models, assesses emerging technologies, presents technical recommendations, collaborates with federal clients, and mentors engineering teams. The role also supports cloud-native development, operations, security, enterprise AI adoption, and experimentation involving modern AI platforms and practices.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Develop advanced personalization and recommendation systems for digital grocery shopping. Build, deploy, monitor, and maintain machine learning pipelines and microservice APIs supporting production inference. Apply natural language processing, generative AI, large language models, agentic AI, recommendation models, deep learning, transformers, and classical machine learning techniques to real-world business problems. Evaluate and tune models, communicate technical results, and contribute to scalable AI solutions.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Own machine learning models end to end, from data preparation and training through evaluation and production deployment. Build pipelines for large multimodal datasets including video, sensor streams, and language. Collaborate with engineering and computer vision teams, rapidly prototype solutions, apply relevant research, and continuously improve production models and tooling.

Own the end-to-end machine learning and LLM lifecycle, taking models and agentic AI workflows from experimentation into reliable, scalable production. Design MLOps infrastructure, serving, orchestration, CI/CD, evaluation, observability, and GPU optimization. Build self-service tooling and establish LLMOps standards while partnering with engineering and business teams. The role is fully remote and involves up to 10% travel.

Sets technical vision for Pinterest’s ML and media infrastructure, leading distributed training, fine-tuning, evaluation, inference, GPU optimization, content ingestion, and signal-processing architectures. Partners with AI/ML, Ads, Content, Shopping, and GenAI teams to productionize reliable, scalable platforms, drive architecture decisions and migrations, improve operational standards, and mentor senior engineers.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Design and build a production-grade AI memory and retrieval engine for personalized, privacy-preserving LLM experiences. Responsibilities include developing multi-tier storage and hybrid retrieval pipelines, memory extraction and classification, adaptive updates, evaluation frameworks, and reliability safeguards. The role researches advanced memory architectures, supports agentic workflows and tool integrations, collaborates with product management, and owns production operations. Strong software engineering, Python, NLP/ML, vector database, LLM orchestration, privacy, and RAG expertise are required.

Build scalable infrastructure for LLM-based agentic AI supporting autonomous security operations. Responsibilities include designing agent orchestration frameworks, LLM inference and resource-management systems, MCP servers, supporting APIs, and distributed backend services. The role collaborates with machine learning, security research, engineering, and product teams. Required experience includes backend infrastructure, distributed systems, APIs, microservices, cloud deployment, containerization, orchestration, and Python, Java, or Go.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.